def make_float_label(true_samples, n_samples):
label = torch.FloatTensor(n_samples).zero_()
label[0:true_samples] = 1
return to_variable(label)
def make_byte_label(true_samples, n_samples):
label = torch.ByteTensor(n_samples).zero_()
label[0:true_samples] = 1
return to_variable(label)
def make_float_label(n_way, n_samples):
label = torch.FloatTensor(n_way * n_samples, n_way).zero_()
for i in range(n_way):
label[n_samples * i:n_samples * (i + 1), i] = 1
return to_variable(label)
def make_long_label(n_way, n_samples):
label = torch.LongTensor(n_way * n_samples).zero_()
for i in range(n_way * n_samples):
label[i] = i // n_samples
return to_variable(label)
def make_float_label(n_way, n_samples):
label = torch.FloatTensor(n_way * n_samples).zero_()
label[0:n_way * n_samples // 2] = 1
return to_variable(label)
def make_byte_label(n_way, n_samples):
label = torch.ByteTensor(n_way * n_samples).zero_()
label[0:n_way * n_samples // 2] = 1
return to_variable(label)